New features

New features in version 3.0 of BayesX are:

A new class of regression objects (mcmcreg objects) has been added. This new class provides the basis for an improved implementation of the fully Bayesian approach and also comprises a number of new model types and specifications (see below).

Distributional regression and Bayesian quantile regression have been added to the available model types.

Multilevel models can be specified to build hierarchical regression specifications.

New features in version 2.1 of BayesX are:

BayesX is now released under the GNU Public License

The BayesX installer now (finally) supports Windows 7

The sourcecode distribution offers increased OS support and should now also work under Mac OS X and 64bit Linux distributions without modifications.

Older versions of BayesX are available from the archive on the download site.

BayesX is now supplemented by the R-package R2BayesX (available from CRAN) that provides easy and convenient access to the BayesX facilities via the usual R-type model specification language.

New features in version 2.0 of BayesX are:

Linux and MacOS Version: In addition to the compiled Windows version of BayesX that includes a graphical user interface, a command line version of BayesX is available that can be compiled with the GNU C++ compiler. This version of BayesX does not have any graphics facilities but can be compiled under any operating system that supports the GNU compiler and in particular under Linuy and Mac OS. Graphics facilities are available in the new BayesX R package, see the next point. A further advantage of the command line version of BayesX is that it can be called from within R using the system command.

R-package with visualization tools: An R-package called BayesX that implements visualisation tools as well as functionality for creating and manipulating map objects (boundary and graph format) has been developed. In the future, this package will also contain the possibility to call BayesX directly from within R, but for the moment it is mostly intended to supplement the command line version of BayesX that does not have graphics facilities.

New regression object with built in varaiable and model selection: A new regression object (stepwisereg object) has been added. The new object implements a penalized least squares (or more generally penalized likelihood) approach for estimating structured additive regression models. It is equipped with built in variable and model selection. The algorithms are able to

Bayesian ridge, lasso and spike and slab priors: For inference based on MCMC, Bayesian regularisation priors have been added to the regression functionality. The methodology supports Bayesian analogues to ridge regression, the lasso and spike and slab priors, see the reference manual for details.

New features in version 1.5 of BayesX are:

New color option for visualizing spatial effects. Specifying option hcl requests a color palette from the HCL colors space. The HCL colors will be selected diverging from a neutral center (grey) to two different extreme colors (red and green) in contrast to the RGB colors diverging from yellow to red and green. HCL colors are particularly useful for electronic presentations since they are device-independent and avoid extremely bright colors. See Sections 6.1 and 9.1.2 of the reference manual for details.

Category-specific effects for cumulative and sequential regression models. Effects in cumulative and sequential regression models for ordered categorical responses can now be requested to be category-specific, see Sections 8.1.1.3 and 8.1.1.4 of the reference manual. Available for remlreg objects only.

Category-specific covariates in multinomial logit models. It is now possible to include parametric as well as nonparametric effects of category-specific covariates into the multinomial logit model, see Section 8.1.1.2 of the reference manual. Available for remlreg objects only.

Varying choice sets in multinomial logit models. To account for the non-availability of some of the categories for specific observations, it is now possible to specify either category-specific offsets or non-availability indicators in multinomial logit models. Details are provided in Section 8.1.1.4 of the reference manual. Available for remlreg objects only.

Centered varying coefficient terms to avoid identification problems. Varying coefficient terms (with either continuous or spatial effect modifier) can be requested to be centered in order to avoid identification restrictions. In particular, this allows to request several varying coefficient terms with the same interaction variable. Details can be found in Section 8.1.1.3 of the reference manual. Available for remlreg objects only.

Continuous time multi-state models. A new class of regression models is available for the estimation of multi-type transition models. This type of models allows to describe the evolution of discrete phenomena in continuous time in terms of transition intensities comparable to the hazard rate of continuous time survival models. Some theoretical background is included in Section 7 of the methodology manual while Sections 7.1.2.6 and 8.1.1.4 of the reference manual describe how to specify multi-state models within BayesX. Multi-state models can be estimated with bayesreg and remlreg objects.

New features in version 1.4 of BayesX are:

Continuous time survival analysis with remlreg objects now allows arbitrary combinations of left, right, and interval censoring as well as left truncation. See Section 8.1.1.4 of the reference manual and Section 5.3 of the methodology manual.

Sequential logit and probit models can be estimated using remlreg objects. See Section 8.1.1.4 of the reference manual.

The manual has been divided into three parts: A reference manual containing a detailed description of the BayesX commands, a methodology manual containing a short review of the methodological background, and a tutorial manual containing tutorial-like Sections for new users.

New features in version 1.3 of BayesX are:

Complex models for continuous time survival analysis based on the Cox model within the mixed model approach (remlreg objects). Compare Section 5.2 of the methodology manual for a description of the theoretical background and Section 8.1 of the reference manual for the BayesX syntax.

Time-varying effects in Cox models (bayesreg and remlreg objects). See Sections 7.1.2.2 and 8.1.1.2 of the reference manual.

New features in version 1.2 of BayesX are:

The priority can be regulated within BayesX using the priority button.

New features in version 1.1 of BayesX are:

Inference for semiparametric regression models with categorial responses based on mixed model methodology (compare Section 8.1.1.4 of the reference manual). This feature allows the estimation of models for multinomial and ordinal responses.

Surface smoothing based on stationary Gaussian random fields (compare Sections 3.2 of the methodology manual and 8.1.1.2 of the reference manual).

New features in version 1.0 of BayesX are:

Regression models for continuous time survival analysis based on the Cox model (bayesreg objects). Compare Section 5.2 of the methodology manual for a description of the theoretical background and

Section 7.1 on how to estimate a cox model using bayesreg objects.

Improved sampling schemes for semiparametric regression with general responses from an exponential family. Proposal densities based on approximations of the full conditional distributions, depending either on the current state of the parameters or the current posterior mode are now available for nearly all types of model terms. Compare Section 4.1 of the methodology manual for details on the different sampling schemes in BayesX.

Inference is then based on estimation procedures for GLMMs, particularly restricted maximum likelihood (REML). From a Bayesian perspective this yields empirical Bayes or posterior mode estimates. Compare chapter 4.2 of the methodology manual for the theoretical background of STAR models and chapter 8 for details on how to use this regression tool.

Tutorials on semiparametric regression using the two regression tools of BayesX. Visit the tutorials page.